The human cornea provides about two-thirds of the refraction of the eye. Thus, its shape is of great interest to optometrists and ophthalmologists who must provide a patient with sharp vision. A device that measures the shape of the cornea is referred to as a corneal topographer. Although there are various methods to measure the cornea, the most popular commercial systems are based on the principle of measuring a pattern reflected off the cornea. The pattern most often used for this purpose is a set of concentric rings. One problem with concentric ring patterns is that it is difficult to know the exact point correspondence between a point on the reflected pattern source and its image reflected off the cornea. If the cornea is not axially symmetric, the surface normal of a point on the cornea will not lie in the meridional plane of the measurement system and thus, the point of light originating on the reflected pattern source will not lie in the same meridional plane. To directly measure the point correspondence, polar and rectangular checkerboard patterns have also been proposed, but have not become popular in commercial systems.
Usually these various reflection patterns are monochrome (black and white), but color has been included in some cases. In one commercial system concentric rings are of alternating colors of red, green, and blue. In a research system, a random color grid was employed. In cases where color is used, the motivation is to provide a more robust method of correctly identifying the correspondence between a point on the reflection pattern source and the image reflected off the cornea and digitized for computer processing.
The reflection patterns to date have been discrete in some way and the computer image processing task is to find edges or peaks in the image which correspond to points in the source. The results of the computer image processing can yield a set of point correspondences that is not always correct. Such image processing errors would lead to large measurement errors in subsequent reconstruction processing. Even if the point correspondence is correct, the accuracy of reconstruction algorithms is related to the spacing of the data. Generally, if the discrete points are far apart the reconstruction error will be larger than would be obtained if the points were close together. Because of this general observation, continuous data would provide the most accurate surface reconstruction measurements.